{"url":"/dataset/random-hierarchy-model","name":"Random Hierarchy Model","full_name":null,"description_markdown":"Artificial hierarchical datasets to study how neural networks learn hierarchical tasks. See papers for details.","description_withheld":null,"homepage":"https://github.com/pcsl-epfl/hierarchy-learning/blob/master/datasets/hierarchical.py","introduced_date":"2023-07-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/how-deep-neural-networks-learn-compositional","title":"How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model","first_author":"Francesco Cagnetta","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["Random Hierarchy Model"],"data_loaders":[],"num_papers_in_archive":10,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}